Search Results (364)

Background/Objectives: A central question in molecular genetics concerns how transcriptional regulatory sequences and de novo genes originate and reach evolutionary fixation. In this study, we utilize the human bicistronic gene SMIM45 as a model to analyze the evolutionary trajectories of gene development. This locus comprises several functional units: three enhancers (one featuring an embedded silencer), an exonic silencer that partially overlaps an ORF, a highly conserved ancestral sequence encoding a 68 aa microprotein, and a human-specific de novo gene encoding a 107 aa protein expressed spatiotemporally in embryonic brain tissues. Methods: The alignment of gene sequences from different species was used to determine the evolutionary development of enhancers and silencers, and the development of the exonic silencer was determined through application of the cultivator model and assessment of nearest-neighbor bases. Results: We identify significant disparities in formation mechanisms; for example, the LOC127896430 NANOG hESC enhancer originated simply via two Alu insertions that constitute the enhancer. In contrast, the exonic silencer (a segment of the LOC130067579 ATAC-STARR-seq lymphoblastoid silent region 13815)—a distinct, novel type of silencer—originated from a combination of diverse mechanisms, including a “cultivator gene” process of base pair fixation, consistent with the cultivator model proposed by Li Zhao and coworkers. Conclusions: SMIM45 exemplifies novel development mechanisms occurring over hundreds of millions of years, culminating in the birth of a human-specific, de novo 107 aa cistron. The associated complex of enhancers and silencers suggests intricate regulation of the 107 aa protein in fetal brain tissues. Full article

This work introduces an approach to evolutionary analysis in which information encoded in amino-acid sequences is converted into a specific type of image, termed a genetic image. Genetic images derived from the amino-acid sequences of cytochrome b and cytochrome c oxidase subunit I are shown to be suitable for identifying evolutionary similarities between organisms. Furthermore, artificial neural networks are demonstrated to recognize these genetic images, enabling identification of species evolution. The results indicate the similarity of the genetic images of organisms belonging to species that emerged earlier during Earth’s evolutionary history to the genetic images of organisms belonging to species that emerged later. This finding indicates that genetic images are inherited and undergo gradual modification during evolutionary processes. The phenomenon of inheritance and modification of genetic images suggests that evolution tends to change the already existing functionalities of organisms, which allows for the ordering of organisms belonging to different species from ancient forms, through species that appeared successively during evolution, to those belonging to species that have developed more recently, up to Homo sapiens. Moreover, unlike analyses based on phylogenetic trees, the method presented in this article does not require computing hypothetical taxonomic units to study evolution. Combined with analyses of the inheritance of genetic images, it can support the interpretations of phylogenetic trees and evolutionary research. Full article

Satellite clock bias (SCB) is a critical error source affecting the positioning and timing accuracy of Global Navigation Satellite Systems (GNSSs). The conventional back propagation neural network (BP) model, when applied to SCB prediction, is prone to local optima and exhibits rapid error divergence. To address these limitations, this study proposes and investigates two enhanced BP models: one optimized by the genetic algorithm (GA) and another by the mind evolutionary algorithm (MEA). A comprehensive comparative analysis is conducted against the standard BP model. Experiments utilize precise clock products from the International GNSS Service (IGS), with data from six representative satellites featuring different atomic clock types (IIR, IIR-M, IIF rubidium, and cesium clocks). The models are trained on 24 h of historical data and evaluated by forecasting clock biases for 2, 6, 12, and 24 h ahead. Prediction accuracy is assessed using root mean square error (RMS), range, and mean error. The results demonstrate that optimization algorithms significantly improve the BP neural network’s performance. The genetic algorithm optimized back propagation neural network (GABP) model demonstrates comprehensive superiority, achieving the highest accuracy across all forecast horizons and satellite types. For instance, in 24 h predictions, the average RMS error of the GABP model (6.516 ns) is merely 10.9% of the standard BP model’s error. Notably, for the cesium clock on satellite G24, the GABP model’s 24 h RMS (1.600 ns) is approximately 23 times lower than that of the mind evolutionary algorithm optimized back propagation neural network (MEABP) model. The GABP model also shows strong adaptability, maintaining high precision for both rubidium and cesium clocks and exhibiting gradual error growth with extended forecast duration, indicating excellent generalization and resistance to overfitting. To further evaluate generalization across different seasons and time periods, additional experiments were conducted using data from February–March, June, and October 2021 on six different satellites. The results consistently show that GABP outperforms MEABP and BP across all tested conditions. While the MEABP model outperforms the standard BP, it shows limitations in long-term forecasts, particularly for cesium clocks, due to tendencies for premature convergence and sensitivity to data noise. In conclusion, the GABP model, leveraging the robust global optimization capability of the genetic algorithm is validated as a highly effective and reliable solution for high-accuracy short- and long-term satellite clock bias prediction. Full article

The remarkable ecological success of fungi is supported by their capacity for rapid and often reversible molecular responses to fluctuating environments. While conventional evolutionary theory has largely emphasized mutation and selection as central drivers of adaptation, many environmentally responsive fungal traits are also shaped by molecular processes that generate reversible phenotypic variation on ecological or developmental timescales. This review synthesizes current knowledge on reversible genetic and epigenetic mechanisms underlying fungal phenotypic plasticity by integrating insights from programmed genetic rearrangements such as mating-type switching, transposable element activity, variation in tandem repeats and the behavior of accessory chromosomes, together with dynamic epigenetic processes including histone modifications, DNA methylation, chromatin remodeling and RNA mediated regulation. Together, these mechanisms form an interconnected framework that enables rapid and, in many cases, reversible phenotypic diversification, although their consequences range from transient regulatory shifts to partially or fully irreversible sequence-level changes. We highlight the molecular machinery that governs reversibility and its evolutionary implications for fungal pathogenesis, symbiosis, and biotechnology. By uniting genetic and epigenetic perspectives, this review advances a holistic framework in which reversibility is treated as a key property of fungal phenotypic plasticity, helping fungi balance stability with flexibility under environmental challenge. Understanding these mechanisms provides new insights into fungal evolution, and opens new avenues for antifungal intervention and the rational design of industrially valuable fungal strains. Full article

In Africa, hares (Lepus spp.) show a high variability in external phenotypes. Species identification, delimitation, and distinction are difficult, due to the generally shallow evolutionary divergence of species, high intraspecific phenotypic variability, potentially complex reticulate evolutionary scenarios, and absence of relevant data across large areas. Our microsatellite and mitochondrial (mt) CR1 sequence data of hares from Tunisia with four different coat color morphs revealed high levels of gene flow both in nuclear and mt gene pools but no relevant partitioning of genetic variability parallel to the four coat color types. Nuclear and mt gene pool correspondence was at a very low level, and no phylogenetic gaps were observed in the mtDNA. Our results indicate only one hare species with different coat colors in Tunisia that shows no signals of reticulate evolution or incipient speciation. Because of potential reticulate evolutionary signals, especially in the mtDNA and the probably high adaptive significance of coat colors and patterns, external phenotypes combined with mtDNA alone may not serve as good proxies for species delimitation in hares from wide ranges in Africa. Combined nuclear and mtDNA population genetic and phylogenetic data allow more complex inferences on current and ancestral evolutionary processes for species identification and delimitation. Full article

Meat quality serves as a pivotal determinant of consumer purchasing behavior and of the economic viability of the livestock industry; as such, research into its regulatory mechanisms is of critical significance for the development of modern agriculture. Traditional investigations into meat quality have predominantly centered on sensory and physicochemical assessments of ultimate phenotypic traits, thereby facing inherent limitations in systematically deciphering the intricate molecular regulatory networks underlying meat quality formation. By contrast, an integrated analysis of the transcriptome and metabolome effectively connects the cascade of “gene transcription—metabolic regulation—phenotypic determination,” which has emerged as a core methodological paradigm in contemporary research on the molecular mechanisms governing meat quality. This review systematically delineates the evolutionary trajectory and principal technological frameworks of meat quality evaluation systems, with a focused synthesis of recent advances achieved through combined transcriptomic and metabolomic analyses in the field of meat quality regulation. The scope of this review encompasses core transcriptional regulatory networks associated with meat quality attributes, pivotal metabolic pathways, signal transduction mechanisms, and protein degradation dynamics. Furthermore, the regulatory impacts exerted by genetic variation among breeds, nutritional modulation, rearing environments, and stress responses on meat quality characteristics are comprehensively elucidated. Integrative analysis reveals that combined transcriptome–metabolome approaches transcend the inherent limitations of single-omics investigations, systematically unraveling the hierarchical regulatory mechanisms governing fundamental meat quality traits, such as muscle fiber type differentiation, postmortem glycolytic progression, intramuscular fat deposition, and flavor compound accumulation. Such integrative strategies have facilitated the identification of functional genes and metabolic biomarkers with potential utility for the early prediction of meat quality outcomes. Concurrently, this review acknowledges persistent challenges confronting the field, including the absence of standardized protocols for multi-omics data integration, insufficient functional causal validation, and a discernible disconnect between research discoveries and practical industrial implementation. Building upon this comprehensive assessment, prospective directions for future multi-omics research in meat quality are proposed, accompanied by the formulation of an integrated end-to-end improvement framework spanning fundamental research, technological innovation, and industrial application. Collectively, this review provides a systematic theoretical foundation for the in-depth elucidation of mechanisms that determine meat quality and the precision-oriented regulation of quality-determining traits in livestock production practices, thereby offering substantial scientific guidance for quality improvement initiatives within the animal husbandry sector. Full article

Hopea celebica Burck is an endangered dipterocarp endemic to Sulawesi, Indonesia, occurring in two ecologically contrasting habitats: karst and ultrabasic forests. These environments differ markedly in soil composition and topography, potentially driving ecological specialization and genetic divergence. To investigate the genetic variation and genetic structure of this species, we applied newly developed microsatellite (SSR) markers, together with the chloroplast DNA sequences of the trnL–trnF region. Genotypes at 15 SSR loci were determined for 255 individuals collected from six populations covering the range of the species’ distribution across karst and ultrabasic forests. Genetic diversity was consistently higher in karst than in ultrabasic populations. DIYABC and VarEff analyses revealed a historical bottleneck and earlier recovery in the karst populations. Analysis of molecular variance (AMOVA) revealed that 35% of the genetic variation was partitioned between habitat types (F_RT = 0.345, p = 0.001). Bayesian clustering (STRUCTURE), principal coordinate analysis (PCoA), and UPGMA dendrograms consistently showed two distinctive clusters corresponding to habitat type. Chloroplast haplotypes differed between populations in the karst and ultrabasic forests. These results suggest that populations in the karst and ultrabasic forests have undergone a long history of differentiation without migration. The strong habitat-related genetic structure likely reflects ecological isolation and early-stage speciation. We recommend treating the karst and ultrabasic populations as distinct conservation units to preserve the evolutionary potential and adaptive capacity of H. celebica under ongoing environmental change. Full article

Sex determination in teleost fishes exhibits remarkable evolutionary plasticity; however, the underlying mechanisms remain largely elusive for many species of high economic importance. Herein, we provide the first genome-wide investigation of the genetic basis of sex determination in the European anchovy (Engraulis encrasicolus), an ecologically and commercially vital clupeiform fish. Using low-pass whole-genome resequencing of 100 sexually mature individuals (50 females and 50 males), we conducted a genome-wide association study (GWAS) and F_ST scans to identify sex-linked loci and characterize sex-determining regions (SDRs). Our analyses revealed two major candidate SDRs located on chromosomes 14 and 18, encompassing multiple sex-associated single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Among these, the amhr2 (anti-Müllerian hormone type 2 receptor) gene on Chr14 displayed the strongest and most consistent association with phenotypic sex, marked by several male-specific missense SNPs and InDel variants. Comparative and transcriptomic analyses confirmed sex-biased expression of amhr2 and other SDR-linked genes, potentially indicating a male heterogametic (XY-like) genetic sex determination system. These results provide the first molecular evidence for a candidate SDR in E. encrasicolus, raise the possibility of involvement of amhr2 and additional loci in sex determination, and highlight rapid sex chromosome turnover within Clupeiformes. Our findings not only expand the understanding of teleost sex determination evolution but also establish a genomic foundation for developing molecular tools for sex identification and population management in anchovy fisheries. Full article

RNA tailing, the non-templated addition of nucleotides to RNA 3′ ends, is a conserved post-transcriptional modification that plays a critical role in regulating RNA metabolism. In plants, this process is primarily mediated by nucleotidyltransferase proteins (NTPs). In this review, we analyze current knowledge of plant NTPs by integrating evidence from genetic, biochemical, and phylogenetic analyses of the gene-family across model plants and crops. We summarize the composition and evolutionary diversification of the plant NTP gene family, with emphasis on lineage-specific expansion and conservation patterns. Using Arabidopsis thaliana as a reference framework, we then describe the molecular roles of NTPs in the tailing of distinct RNA classes, emphasizing how tail type and length confer context-dependent regulatory outcomes including stabilization versus degradation and processing/maturation versus clearance. We further examine the determinants of substrate choice, focusing on RNA type, terminal structure, and subcellular localization. Finally, we discuss the biological functions of NTP-mediated RNA tailing in plants, linking RNA tailing to development, stress responses, antiviral immunity, and agronomic traits in crops. We conclude by outlining key mechanistic and physiological challenges that define future directions for understanding and harnessing NTP-mediated RNA regulation. Collectively, this review provides an integrated framework for understanding how RNA tailing by NTPs shapes plant RNA metabolism and biological fitness. Full article

Antimicrobial resistance (AMR) has become a major global public health challenge, and widely residual disinfectants in the environment are one of the key drivers of bacterial AMR development. This study aimed to investigate the inductive effects of three commonly used disinfectants—benzalkonium bromide (BAB), glutaraldehyde (GTA), and povidone-iodine (PVP-I)—on the resistance of Escherichia coli (E. coli), as well as the resultant bacterial phenotypic and genetic alterations. Three disinfectants frequently detected in clinical and environmental settings were selected as the research objects: first, their bactericidal efficacy against environmental bacteria was determined; subsequently, a concentration-increasing gradient approach was adopted to systematically explore the evolutionary patterns of E. coli resistance under the stress of sub-inhibitory concentrations (SICs). After induction, the bacterial resistance levels to disinfectants and various antibiotics, growth characteristics, and biofilm-forming ability were detected, and combined with whole-genome analysis to investigate genetic-level changes. The results showed that all three disinfectants could enhance E. coli resistance to themselves (12–48-fold) and antibiotics, and the induced antibiotic resistance exhibited favorable genetic stability. Among them, BAB induced the strongest resistance, with the most significant increase in resistance levels to multiple antibiotics (16–64-fold); GTA had the weakest inductive effect, only slightly enhancing bacterial resistance to a small number of antibiotics. Notably, all induced strains exhibited reduced growth rates yet markedly enhanced biofilm-forming capacity, alongside acquired genomic structural variations. Their gene functions displayed shared adaptive signatures in coping with environmental stress, while core pathogenicity-associated genes remained conserved. This study demonstrates that inducing E. coli using environmentally relevant low concentrations of disinfectant residues as initial induction doses drives the evolution of bacterial antimicrobial resistance (AMR), with distinct resistance induction risks among the three disinfectant types. These findings offer critical insights for standardizing disinfectant application, mitigating the transmission of bacterial AMR, and underscore the imperative of interdisciplinary collaboration to tackle the environmental risks posed by disinfectant residues. Full article

Coilia nasus, a typical species with migratory–sedentary polymorphism, shows different intestinal microbiota characteristics among its different ecotypes. This is attributed to differences in feeding habits and habitat environments (such as water temperature, salinity, etc.). This study constructed a database of intestinal microbiota for three ecological types of C. nasus, namely migratory type (comprising marine populations and freshwater populations), sedentary type and aquaculture-reared type, through 16S rRNA amplicon sequencing technology. This study investigates the ecological mechanisms underlying microbiota differentiation, focusing on three key drivers: environmental selection, host nutritional metabolism requirements, and host life history strategies. The results showed that the core flora of C. nasus consisted of Firmicutes, Proteobacteria, and Actinobacteria. Both the depletion of microbial taxa and the enrichment of marine-adapted bacterial lineages—including Proteobacteria and Psychrobacter—are associated with elevated salinity in the migratory marine population of C. nasus. In contrast, the elevated relative abundance of Actinobacteria in aquaculture-reared C. nasus is likely attributable to dietary supplementation with protein- and lipid-rich artificial feed. Functional correlation analysis holds promise for partially predicting the microbiota’s metabolic functional succession patterns. The dominance of Pseudomonas_E in the migratory freshwater population is consistent with its well-documented physiological versatility and adaptive capacity in dynamically fluctuating aquatic habitats. The elevated abundance of Cyanobacteria in the sedentary population C. nasus coincides with the water bloom in their habitat, suggesting that the structure of the microbiota may serve as a novel biomarker for indicating the ecosystem. In conclusion, this study identifies potential molecular markers for tracing genetic resources and distinguishing ecological types of C. nasus, while establishing a theoretical foundation for elucidating the co-evolutionary dynamics between fish hosts and their associated microbiota—and thereby informing both conservation strategies for wild populations and microbiota-informed aquaculture practices. Full article

Carbapenem-resistant Enterobacter cloacae Complex (CRECC) has emerged as an important multidrug-resistant pathogen in healthcare settings, although it has historically received less attention than carbapenem-resistant Klebsiella pneumoniae and other major carbapenem-resistant Enterobacterales (CRE). Recent epidemiological reports from several regions indicate increasing detection rates of CRECC in tertiary hospitals, where it is associated with bloodstream infections, pneumonia, urinary tract infections, and prolonged hospitalization. The dissemination of carbapenemase genes, particularly bla_NDM, bla_KPC, and bla_OXA-48-like, carried predominantly on conjugative plasmids (e.g., IncFII, IncX3, IncL), represents the primary resistance mechanism, often accompanied by porin loss and efflux pump overexpression. High-risk clones such as ST171 and ST78 contribute to nosocomial persistence and outbreak potential. Beyond clinical settings, CRECC and related resistance determinants have been reported in companion animals, livestock, food products, wastewater systems, and natural aquatic environments. Although most available studies examine these sectors separately, the recurring detection of genetically related resistance genes and plasmid types suggests potential epidemiological links that warrant integrated surveillance. Environmental reservoirs, particularly hospital effluents and wastewater treatment systems, may facilitate the maintenance and dissemination of resistance genes. This review synthesizes current evidence on the epidemiology, resistance mechanisms, and evolutionary dynamics of CRECC in human, animal, and environmental contexts under a One Health framework. A better understanding of its ecological distribution and genetic plasticity is essential to inform coordinated surveillance strategies and mitigate the public health risks associated with the continued spread of carbapenem resistance. Full article

Nearest-neighbor classifiers are accurate and easy to deploy, but their memory footprint and inference time grow with the size of the reference set. This paper studies an evolutionary prototype selection strategy for k-nearest neighbor (K-NN) classification aimed at extreme, class-balancedreduction. A compact genetic algorithm (GA) evolves a fixed number of prototype indices per class drawn from a disjoint design partition; the selected prototypes are then used by a 1-NN classifier, with fitness defined as the number of correctly classified test instances. To address concerns about generality and baseline strength, we evaluate an experimental suite including synthetic 2D Gaussians ($\sigma =0.5$ and $\sigma =1.0$) and a 3D three-moons geometry, as well as public benchmarks spanning binary and multi-class settings and higher-dimensional data (Breast Cancer Wisconsin, Wine, Reduced MNIST/Digits 8 × 8, Forest CoverType with seven classes, and a 10D five-class spiral benchmark). We compare against K-NN baselines with $k\in \{1,3,5,7\}$ using all design samples, and include GA operator ablations (GA1/GA2/GA3). Each scenario is repeated over 30 independent runs, reporting mean ± std, min/max, per-run distributions, win/tie/loss counts, and non-parametric significance tests (paired Wilcoxon with Holm correction; Friedman where applicable). Across datasets, the GA-selected prototype banks—often orders of magnitude smaller than the full design set—match or improve accuracy, with frequent statistically supported wins against strong K-NN baselines, and in the hardest cases provide substantial compression with no loss relative to the best baseline. These results establish a reproducible baseline for extreme, class-balanced prototype reduction suitable for memory- and latency-constrained deployments and for fair comparison against more elaborate prototype selection methods. Full article

Background/Objectives: FAM111A is a trypsin-like serine protease that has emerged as a regulator of DNA replication, and is directly related to genome stability, protein homeostasis, antiviral defense and cancer progression. Pathogenic variants in FAM111A are correlated with genetic syndromes such as Kenny–Caffey syndrome type 2 (KCS2) and gracile bone dysplasia/osteocraniostenosis (GCLEB/OCS). This study focuses on the evolutionary, genetic, and structural analysis of FAM111A, in order to identify key regions and candidate pharmacological targets that are related to this enzyme’s function. Methods: The methodology of this in silico study includes separate analyses at the sequence, structural and functional levels. Initially, data mining was carried out using NCBI/Protein (2025), and then data filtering was performed in order to identify representative FAM111A sequences for several species. Sequence analysis was then executed through multiple alignments and phylogenetic analyses. Through this, conserved domains and motifs were identified. For structural analysis, human pathogenic mutations and protein structures were identified through searches in biological databases including PDB and ClinVar, and then all data were analyzed in order to identify candidate pharmacological targets related to FAM111A function. Results: Approximately 1850 FAM111A protein sequences were retrieved for several species, and after filtering processes a dataset of 85 representative sequences was generated. Evolutionary analysis indicates that FAM111A originated in early metazoans, with progressive domain specialization leading to mammal-restricted acquisition of regulatory elements, including the PIP-box PCNA (proliferating cell nuclear antigen) interacting peptide and UBL (ubiquitin-like) domains. The ubiquitin-like/DNA binding domain and catalytic serine protease domain (SPD) are the most conserved, containing seven highly conserved motifs. The structural analysis was based on two protein structures and 34 critical mutations that accumulate in two distinct regions. Finally, by combining the results, six pharmacological targets and 100 inhibitors are proposed. Conclusions: Advancing the structural and function characterization of FAM111A, coupled with pharmacological target identification and evolutionary insights, will be critical to validate this underexplored protease as a therapeutic genetic target in genetic disorders, cancer, and antiviral responses. Full article

Gibberellins (GAs) play a crucial regulatory role in the growth and development of cotton (Gossypium hirsutum L.). Through bioinformatics analyses, we identified a total of 39 GA2ox genes (encoding gibberellin 2-oxidases) in the cotton genome, designated GhGA2ox1 to GhGA2ox39. Based on phylogenetic analysis, these genes were classified into five groups. We further examined their gene structures, conserved motifs, and chromosomal distributions, revealing that members within the same group shared similar structural and motif organizations. Collinearity and cis-element analyses provided important insights into the evolutionary history and regulatory potential of the GA2ox gene family in cotton. Notably, using nucleotide diversity (π) and population differentiation (F_ST) analyses across the entire family, we screened and identified nine candidate genes that underwent strong artificial selection during cotton domestication and improvement. Further haplotype-phenotype association analysis identified GH_D09G0919 (GhGA2ox31) as a key regulator of Plant Height (PH). To validate their regulatory roles, we analyzed the genotype distribution in accessions with extreme phenotypes. The results revealed divergent selection histories for these two loci: the favorable allele of GH_D01G0720 (GhGA2ox23) was already fixed in the tested population, whereas GH_D09G0919 maintained significant natural variation. Specifically, the Hap2 allele of GH_D09G0919 was significantly enriched in the shortest accessions compared to the tallest ones. Importantly, quantitative real-time polymerase chain reaction (qRT-PCR) analysis confirmed that the Hap2 allele drives significantly higher gene expression in leaves, suggesting that enhanced GA catabolism underlies the compact phenotype. Additionally, transcriptomic profiling revealed the tissue-specific expression patterns of candidate genes, implying their functional roles in development. Furthermore, functional validation using the Arabidopsis mutant of the homologous gene (AtGA2ox8) confirmed its conserved role in regulating plant height, as the mutant exhibited a distinct short-stature phenotype. These results uncover valuable genetic resources for molecular breeding to shape compact cotton architecture. Collectively, this study aims to analyze the evolutionary patterns of the cotton GA2ox gene family and to identify key genes that regulate plant height under artificial selection, providing theoretical support for molecular breeding of compact plant types. Full article